Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for predicting energy consumption of a vehicle using a statistical model, said method comprising: obtaining a plurality of input vectors for said vehicle at defined time intervals at a plurality of points in time, wherein each input vector is associated with each point in time of said plurality of points in time; capturing an energy level associated with each input vector of said plurality of input vectors at each point in time for said vehicle, wherein said energy level corresponds to at least one of a stored battery power and a stored fuel level of said vehicle; predicting a change in said energy level using a processor and said statistical model, wherein (i) the change in said energy level comprises a function of corresponding input vectors and an associated weight vector, (ii) said weight vector is derived using said plurality of input vectors and associated energy levels at each point in time of said plurality of points in time, and represents an overall effect of each said input vector on energy consumption of said vehicle, and (iii) said change in said energy level is predicted through a regression analysis of said energy level associated with each said input vector; and providing results corresponding to the predicted change in said energy level to an audio-video output unit of said vehicle.
A method for predicting a vehicle's energy consumption using a statistical model. The method involves collecting input vectors at different times, where each vector contains variables affecting energy use (like speed, location, temperature). The vehicle's energy level (battery or fuel) is recorded for each input vector. A processor then predicts energy change using a statistical model that relates the input vectors to the energy level. This model uses a weight vector, derived from the collected data, representing each variable's impact on energy consumption. A regression analysis is used to predict the change. The predicted energy change is displayed on the vehicle's audio-video output.
2. The method of claim 1 , wherein said weight vector associated with said input vector is derived using a linear regression that derives said weight vector based on said plurality of input vectors and respective energy levels at said plurality of points in time.
The method for predicting a vehicle's energy consumption using a statistical model, where a weight vector associated with an input vector is derived using a linear regression based on collected input vectors (like speed, location, temperature) and respective energy levels (battery or fuel) at different points in time. This linear regression determines the weight vector, which represents each variable's impact on energy consumption and is then used within the statistical model to predict energy changes.
3. The method of claim 2 , further comprising: predicting a set of input vectors at defined time intervals at a plurality of future points in time based on a subset of said plurality of input vectors generated at said defined time intervals, at said plurality of points in time, wherein said subset of said plurality of input vectors represents the most recent input vectors of said vehicle; deriving a change in said energy level for said plurality of future points in time using said statistical model, wherein said change in said energy level is derived by adding a change in energy level for each defined time interval; capturing an actual change in energy level for each point in time of said plurality of future points in time, wherein said actual change in said energy level is based on said energy level of said vehicle associated with each input vector corresponding to each point in time; computing a difference between the derived change in said energy level and said actual change in said energy level; and refining said weight vector for minimizing the difference between the derived change in said energy level and said actual change in said energy level, wherein refining said weight vector comprises modifying the value of the weight vector to minimize the difference, wherein said statistical model is refit in response to the refined weight vector.
The method for predicting a vehicle's energy consumption using a statistical model refines the model over time. First, it predicts future input vectors (like speed, location, temperature) based on recent past input vectors. Then, it predicts future energy changes by summing up predicted energy changes for each time interval. The actual energy change is measured. The difference between predicted and actual energy change is calculated. Finally, the weight vector (representing each variable's impact) is adjusted to minimize this difference, refining the statistical model. The model is then retrained using the refined weight vector.
4. The method of claim 1 , wherein said each input vector comprises a plurality of sensor data and a plurality of database data, wherein said plurality of sensor data is captured for at least one of a vehicle location environment, a vehicle equipment profile, and a driver behavior profile at each point in time of said plurality of points in time, wherein said plurality of sensor data is obtained from a plurality of sensors coupled to said vehicle, wherein said plurality of database data is obtained for at least one of a vehicle location environment, a vehicle equipment profile, and a driver behavior profile for a plurality of vehicles, and wherein said plurality of database data is obtained from a database storing previously recorded data for at least one of said vehicle location environment, said vehicle equipment profile, and said driver behavior profile corresponding to said plurality of vehicles.
In the method for predicting a vehicle's energy consumption, each input vector consists of sensor data (from vehicle sensors) and database data (from external databases). Sensor data, related to vehicle location, equipment, or driver behavior, is captured at each time point. Database data, similarly related to location, equipment, or driver behavior but for many vehicles, is retrieved from a database storing historical data. This combined data provides a comprehensive picture for the statistical model to predict energy consumption.
5. The method of claim 4 , wherein said plurality of sensor data correspond to at least one of location data, time data, day data, solar radiation data, temperature data, humidity data, barometric pressure data, wind speed data, wind direction data, fuel level data, driving pattern data, and driver identity data associated with said vehicle and an environment around said vehicle.
In the method using sensor data to predict a vehicle's energy consumption, the sensor data includes information such as location, time, day, solar radiation, temperature, humidity, barometric pressure, wind speed, wind direction, fuel level, driving patterns, and driver identity. These sensor readings from the vehicle and its environment are used to create input vectors for the statistical model to predict energy consumption.
6. The method of claim 4 , wherein said plurality of sensors correspond to at least one of a tire pressure sensor, a regenerative braking sensor, a battery capacity sensor, a battery charge sensor, a solar radiation sensor, a humidity sensor, a temperature sensor, a barometric pressure sensor, a motor temperature sensor, a lubrication level sensor, a wind resistance sensor, a proximity sensor, a weight sensor, an identity sensor, and a set of environmental sensors.
In the method using sensor data to predict a vehicle's energy consumption, the sensors used include tire pressure sensors, regenerative braking sensors, battery capacity sensors, battery charge sensors, solar radiation sensors, humidity sensors, temperature sensors, barometric pressure sensors, motor temperature sensors, lubrication level sensors, wind resistance sensors, proximity sensors, weight sensors, identity sensors, and environmental sensors. The data from these sensors provides detailed information for predicting the vehicle's energy usage.
7. The method of claim 4 , wherein said plurality of database data corresponds to at least one of weather data, route data, traffic data, and driving pattern data of a plurality of drivers.
In the method utilizing database data to predict a vehicle's energy consumption, the database data consists of weather data, route data, traffic data, and driving patterns of multiple drivers. This external data complements the vehicle's sensor data, improving the accuracy of the energy consumption prediction.
8. The method of claim 1 , wherein said statistical model comprises at least one of a linear function, a quadratic function, a periodic function, and a rule based function of at least one of a stored energy of the vehicle at each point in time, each vehicle input vector, and each database input vector for each defined time interval, wherein said database input vector is generated based on at least one of a plurality of environmental data and a road condition information.
In the method for predicting a vehicle's energy consumption, the statistical model used can be a linear, quadratic, periodic, or rule-based function. This function uses the vehicle's stored energy at each point in time, input vectors (sensor data), and database input vectors (environmental/road data). The database input vector is generated using environmental data and road conditions, helping to refine the prediction model.
9. A system for predicting energy consumption of a vehicle using a statistical model, said system comprising: an acquisition module that obtains a plurality of input vectors at defined time intervals at a plurality of points in time; an energy meter that captures an energy level associated with each input vector of said plurality of input vectors at each point in time for said vehicle, wherein said energy meter captures said energy level by capturing at least one of a stored battery power and a stored fuel level of said vehicle; a processor that predicts a change in energy level using said statistical model, wherein (i) said change in energy comprises a function of corresponding input vectors and an associated weight vector, wherein (ii) said weight vector is derived using said plurality of input vectors and associated energy level at each point in time of said plurality of points in time, and represents an overall effect of each said input vector on energy consumption of the vehicle, and (iii) said change in energy level is predicted through a regression analysis of said energy level associated with said each input vector; and an output unit that displays results corresponding to the predicted change in said energy level of said vehicle.
A system for predicting a vehicle's energy consumption using a statistical model consists of an acquisition module that collects input vectors (like speed, location) at time intervals. An energy meter measures the vehicle's energy level (battery or fuel) for each input vector. A processor predicts energy change using a statistical model relating input vectors to energy level. This model utilizes a weight vector (representing each variable's impact), derived from the data. The processor predicts energy change through regression analysis. An output unit displays the predicted energy change.
10. The system of claim 9 , wherein said processor derives said weight vector associated with said input vector using linear regression of said energy level associated with each input vector at each point in time.
The system for predicting a vehicle's energy consumption where the processor derives the weight vector (representing each variable's impact) associated with the input vector using linear regression of the energy level (battery or fuel) associated with each input vector at each point in time. This linear regression provides the weights used in the statistical model for energy prediction.
11. The system of claim 9 , wherein said processor: predicts a set of input vectors at defined time intervals at a plurality of future points in time based on a subset of said plurality of input vectors generated at said defined time intervals; captures an actual change in energy level for each point in time of said plurality of future points in time, wherein said actual change in said energy level is based on said energy level of said vehicle associated with each input vector corresponding to each point in time; computes a difference between a derived change in said energy level and said actual change in said energy level; and refines said weight vector for minimizing a difference between said derived change in said energy level and said actual change in said energy level, wherein refining said weight vector comprises modifying a value of said weight vector to minimize a difference, wherein said statistical model is refit in response to the refined weight vector.
The system for predicting a vehicle's energy consumption refines its prediction model over time. The processor predicts future input vectors (like speed, location) based on recent past input vectors. It measures the actual energy change. It calculates the difference between predicted and actual energy change. Then, it adjusts the weight vector (representing variable impact) to minimize this difference, refining the model. The statistical model is then retrained.
12. The system of claim 9 , wherein said acquisition module acquires a plurality of sensor data for at least one of a vehicle location environment, a vehicle equipment profile, and a driver behavior profile at each point in time of said plurality of points in time, wherein said plurality of sensor data is obtained from a plurality of sensors coupled to said vehicle, wherein said plurality of database data is obtained for at least one of a vehicle location environment, a vehicle equipment profile, and a driver behavior profile for a plurality of vehicles, and wherein said plurality of database data is obtained from a database storing previously recorded data for at least one of said vehicle location environment, said vehicle equipment profile, and said driver behavior profile corresponding to said plurality of vehicles.
In the system for predicting a vehicle's energy consumption, the acquisition module collects sensor data (from vehicle sensors) and database data (from external databases). Sensor data relates to location, equipment, or driver behavior. Database data, similarly related but for many vehicles, is retrieved from a database storing historical data. This combined data provides a comprehensive picture for the statistical model.
13. The system of claim 12 , wherein said acquisition module acquires said plurality of sensor data from a plurality of sensors that correspond to at least one of a tire pressure sensor, a regenerative braking sensor, a battery capacity sensor, a battery charge sensor, a solar radiation sensor, a humidity sensor, a temperature sensor, a barometric pressure sensor, a motor temperature sensor, a lubrication level sensor, a wind resistance sensor, a proximity sensor, a weight sensor, an identity sensor, and a set of environmental sensors.
In the system that uses sensor data to predict a vehicle's energy consumption, the acquisition module obtains sensor data from sensors such as tire pressure sensors, regenerative braking sensors, battery capacity sensors, battery charge sensors, solar radiation sensors, humidity sensors, temperature sensors, barometric pressure sensors, motor temperature sensors, lubrication level sensors, wind resistance sensors, proximity sensors, weight sensors, identity sensors, and environmental sensors.
14. The system of claim 9 , wherein said acquisition module acquires said plurality of database data corresponding to at least one of weather data, route data, traffic data, and driving pattern data of a plurality of drivers.
In the system for predicting a vehicle's energy consumption, the acquisition module obtains database data from external sources. This data includes weather data, route data, traffic data, and driving patterns of multiple drivers. This external data complements the vehicle's sensor data, improving the accuracy of the energy consumption prediction.
15. The system of claim 9 , wherein said processor utilizes said statistical model comprising at least one of a linear function, a quadratic function, a periodic function, and a rule based function of at least one of a stored energy of said vehicle at each point in time, each vehicle input vector, and each database input vector for each defined time interval, and wherein said database input vector is generated based on at least one of a plurality of environmental data and road condition information.
In the system for predicting a vehicle's energy consumption, the processor uses a statistical model comprising linear, quadratic, periodic, or rule-based functions. These functions use the vehicle's stored energy, input vectors (sensor data), and database input vectors (environmental/road data). The database input vector is generated from environmental and road condition information.
16. A non-transitory program storage device readable by a computer, and comprising a program of instructions executable by said computer to perform a method for predicting energy consumption of a vehicle using a statistical model, said method comprising: obtaining a plurality of input vectors for said vehicle at defined time intervals at a plurality of points in time, wherein each input vector is associated with each point in time of said plurality of points in time; capturing an energy level associated with each input vector of said plurality of input vectors at each point in time for said vehicle, wherein said energy level corresponds to at least one of a stored battery power and a stored fuel level of said vehicle; predicting a change in said energy level using said statistical model, wherein (i) the change in said energy level comprises a function of corresponding input vectors and an associated weight vector, (ii) said weight vector is derived using said plurality of input vectors and associated energy level at each point in time of said plurality of points in time, and represents an overall effect of each said input vector on energy consumption of said vehicle, and (iii) said change in said energy level is predicted through a regression analysis of said energy level associated with each said input vector; and providing results corresponding to the predicted change in said energy level to an output unit of said vehicle.
A non-transitory computer program stored on a device predicts a vehicle's energy consumption using a statistical model. The program collects input vectors (like speed, location) at different times and records the vehicle's energy level (battery or fuel) for each input vector. It predicts energy change using a statistical model relating input vectors to energy level, using a weight vector (representing variable impact). The prediction uses regression analysis, and the predicted energy change is outputted.
17. The program storage device of claim 16 , wherein said weight vector associated with said input vector is derived using a linear regression that derives said weight vector based on said plurality of input vectors and respective energy levels at said plurality of points in time.
The computer program for predicting a vehicle's energy consumption where the weight vector (representing each variable's impact) associated with the input vector is derived using a linear regression. This linear regression determines the weight vector based on collected input vectors (like speed, location, temperature) and respective energy levels (battery or fuel) at different points in time.
18. The program storage device of claim 17 , wherein said method further comprises: predicting a set of input vectors at defined time intervals at a plurality of future points in time based on a subset of said plurality of input vectors generated at said defined time intervals, at said plurality of points in time, wherein said subset of said plurality of input vectors represents the most recent input vectors of said vehicle; deriving a change in said energy level for said plurality of future points in time using said statistical model, wherein said change in said energy level is derived by adding a change in energy level for each defined time interval; capturing an actual change in energy level for each point in time of said plurality of future points in time, wherein said actual change in said energy level is based on said energy level of said vehicle associated with each input vector corresponding to each point in time; computing a difference between the derived change in said energy level and said actual change in said energy level; and refining said weight vector for minimizing the difference between the derived change in said energy level and said actual change in said energy level, wherein refining said weight vector comprises modifying the value of the weight vector to minimize the difference, wherein said statistical model is refit in response to the refined weight vector.
The computer program for predicting a vehicle's energy consumption refines its model over time. It predicts future input vectors (like speed, location) based on recent past vectors. It predicts future energy changes by summing predicted changes. It measures actual energy change and calculates the difference between predicted and actual change. It adjusts the weight vector (representing variable impact) to minimize this difference, refining the statistical model, which is then retrained.
19. The program storage device of claim 16 , wherein said each input vector comprises a plurality of sensor data and a plurality of database data, wherein said plurality of sensor data is captured for at least one of a vehicle location environment, a vehicle equipment profile, and a driver behavior profile at each point in time of said plurality of points in time, wherein said plurality of sensor data is obtained from a plurality of sensors coupled to said vehicle, wherein said plurality of database data is obtained for at least one of a vehicle location environment, a vehicle equipment profile, and a driver behavior profile for a plurality of vehicles, and wherein said plurality of database data is obtained from a database storing previously recorded data for at least one of said vehicle location environment, said vehicle equipment profile, and said driver behavior profile corresponding to said plurality of vehicles.
The computer program for predicting a vehicle's energy consumption uses input vectors comprising sensor data (from vehicle sensors) and database data (from external databases). Sensor data, related to location, equipment, or driver behavior, is captured. Database data, similarly related but for many vehicles, is retrieved from a database. This provides comprehensive data for the statistical model.
20. The program storage device of claim 16 , wherein said statistical model comprises at least one of a linear function, a quadratic function, a periodic function, and a rule based function of at least one of a stored energy of the vehicle at each point in time, each vehicle input vector, and each database input vector for each defined time interval, wherein said database input vector is generated based on at least one of a plurality of environmental data and a road condition information.
The computer program for predicting a vehicle's energy consumption uses a statistical model that can be linear, quadratic, periodic, or rule-based. The function uses vehicle energy, input vectors (sensor data), and database input vectors (environmental/road data), the latter generated from environmental data and road conditions.
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December 12, 2017
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